# ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility

arXiv: https://arxiv.org/abs/2012.01172 · HTML: https://mlsanity.com/paper/2012.01172/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers](https://mlsanity.com/paper/2406.14325/) (2024)
- [Reproducibility in Machine Learning-Driven Research](https://mlsanity.com/paper/2307.10320/) (2023)
- [Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence](https://mlsanity.com/paper/2111.00826/) (2021)
- [Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing](https://mlsanity.com/paper/2606.00033/) (2026)
- [Supporting Artifact Evaluation with LLMs: A Study with Published Security Research Papers](https://mlsanity.com/paper/2603.06862/) (2026)

## Abstract (arXiv)

We present ReproducedPapers.org: an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students and the added value of an online reproduction repository among AI researchers. We use anonymous self-assessment surveys and obtained 144 responses. Results suggest that students who do a reproduction project place more value on scientific reproductions and become more critical thinkers. Students and AI researchers agree that our online reproduction repository is valuable.